{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX D MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Apr 2025, 18:32:33
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. *** APPENDIX D MODELS: ANALYZING SENSITIVITY OF MANUSCRIPT MODEL ESTIMATES -- OMITTING 2nd IT MODERNIZATION REFORMS [NEBRASKA AND NEW MEXICO] & 2020-2022 [COVID YEARS] ***
. 
. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. 
. *** MODELS PREDICTING VARIOUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
. 
. 
. 
. 
. 
. *** MODEL 1: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***
. 
. *  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *
. 
. 
. 
. *** MODEL 2: ABSOLUTE TYPE I ERROR RATE ***
. 
. * [overpayment error rate / paid claims sample] *
. 
. 
. 
. 
. *** MODEL 3: RELATIVE TYPE I ERROR RATE:  {c -(}TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]{c )-}      (SAMPLE WEIGHTED)  ***
. 
. *  {c -(}[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]{c )-}  *
. 
. 
. 
. 
. 
. 
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. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** RETRIEVE MANUSCRIPT MODELS DATABASE [as of 04-10-2025] ***
. 
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", clear
{txt}
{com}. 
. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
. 
. 
. 
. **** TABLE D1 -- MODELS D1-D3: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" APPENDIX D STATISTICAL ANALYSES [APRIL 2025]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [TOTAL PROGRAM ERROR RATE] **** 
. 
. 
. ** (MODEL D1; FIGURES D1A-D1C; MODEL D2: FIGURES D2A-D2C; MODEL D3: FIGURES D2D-D2F) **
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS, EXCLUDING YEAR 2002 & 2020-2022] ***
. 
. 
. 
. ** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS D1 & D3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL D2] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      5,987
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      5,987

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1106655
                        {txt}Largest       Std. dev.     {res}   .10426
{txt}75%    {res} .1444445       .7738096
{txt}90%    {res} .2246022       .7785548       {txt}Variance      {res} .0108702
{txt}95%    {res} .3010511        .781746       {txt}Skewness      {res} 2.341965
{txt}99%    {res} .5555556       .7857143       {txt}Kurtosis      {res}  10.8711
{txt}
{com}. di r(p75)
{res}.14444445
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen tot_interstate_catD =.
{txt}(12,551 missing values generated)

{com}. replace tot_interstate_catD = 0 if tot_interstate<= r(p25) 
{txt}(2,546 real changes made)

{com}. replace tot_interstate_catD = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,587 real changes made)

{com}. replace tot_interstate_catD = 2 if tot_interstate>= r(p75) 
{txt}(4,418 real changes made)

{com}. *
. tab tot_interstate_catD if e(sample)

{txt}tot_interst {c |}
   ate_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,537       25.67       25.67
{txt}          1 {c |}{res}      2,952       49.31       74.98
{txt}          2 {c |}{res}      1,498       25.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,987      100.00
{txt}
{com}. tab tot_interstate_catD if e(sample) & itmod_adopt_state==1

{txt}tot_interst {c |}
   ate_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,537       25.67       25.67
{txt}          1 {c |}{res}      2,952       49.31       74.98
{txt}          2 {c |}{res}      1,498       25.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,987      100.00
{txt}
{com}. *
. *
. *
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt   if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. *
. *
. sum t1_interstate if e(sample), detail

                        {txt}t1_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      6,016
{txt}25%    {res}      .02              0       {txt}Sum of wgt. {res}      6,016

{txt}50%    {res} .0408163                      {txt}Mean          {res} .0550208
                        {txt}Largest       Std. dev.     {res} .0586169
{txt}75%    {res} .0769231       .4117647
{txt}90%    {res}     .125       .4222222       {txt}Variance      {res} .0034359
{txt}95%    {res} .1666667       .4285714       {txt}Skewness      {res} 2.069071
{txt}99%    {res} .2857143       .4285714       {txt}Kurtosis      {res} 9.234398
{txt}
{com}. di r(p75)
{res}.07692308
{txt}
{com}. di r(p25)
{res}.02
{txt}
{com}. *
. gen t1_interstate_catD =.
{txt}(12,551 missing values generated)

{com}. replace t1_interstate_catD = 0 if t1_interstate<= r(p25) 
{txt}(2,703 real changes made)

{com}. replace t1_interstate_catD = 1 if t1_interstate> r(p25) & t1_interstate < r(p75) 
{txt}(5,879 real changes made)

{com}. replace t1_interstate_catD = 2 if t1_interstate>= r(p75) 
{txt}(3,969 real changes made)

{com}. *
. tab t1_interstate_catD if e(sample)

{txt}t1_intersta {c |}
    te_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,511       25.12       25.12
{txt}          1 {c |}{res}      2,994       49.77       74.88
{txt}          2 {c |}{res}      1,511       25.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,016      100.00
{txt}
{com}. tab t1_interstate_catD if e(sample) & itmod_adopt_state==1

{txt}t1_intersta {c |}
    te_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,511       25.12       25.12
{txt}          1 {c |}{res}      2,994       49.77       74.88
{txt}          2 {c |}{res}      1,511       25.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,016      100.00
{txt}
{com}. *
. *
. *
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt   adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      5,798
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      5,798

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1099698
                        {txt}Largest       Std. dev.     {res} .1027292
{txt}75%    {res} .1435897       .7738096
{txt}90%    {res} .2222222       .7785548       {txt}Variance      {res} .0105533
{txt}95%    {res} .2985154        .781746       {txt}Skewness      {res} 2.338546
{txt}99%    {res} .5555556       .7857143       {txt}Kurtosis      {res} 10.94796
{txt}
{com}. di r(p75)
{res}.14358975
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen relt1_interstate_catD =.
{txt}(12,551 missing values generated)

{com}. replace relt1_interstate_catD = 0 if tot_interstate<= r(p25) 
{txt}(2,546 real changes made)

{com}. replace relt1_interstate_catD = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,568 real changes made)

{com}. replace relt1_interstate_catD = 2 if tot_interstate>= r(p75) 
{txt}(4,437 real changes made)

{com}. *
. tab relt1_interstate_catD if e(sample)

{txt}relt1_inter {c |}
 state_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,486       25.63       25.63
{txt}          1 {c |}{res}      2,861       49.34       74.97
{txt}          2 {c |}{res}      1,451       25.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,798      100.00
{txt}
{com}. tab relt1_interstate_catD if e(sample) & itmod_adopt_state==1

{txt}relt1_inter {c |}
 state_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,486       25.63       25.63
{txt}          1 {c |}{res}      2,861       49.34       74.97
{txt}          2 {c |}{res}      1,451       25.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,798      100.00
{txt}
{com}. *
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. ** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS D1 & D3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL D2] **
. 
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1304348              0
{txt} 5%    {res} .2138889              0
{txt}10%    {res} .2746295              0       {txt}Obs         {res}      5,987
{txt}25%    {res} .3914894              0       {txt}Sum of wgt. {res}      5,987

{txt}50%    {res} .5357143                      {txt}Mean          {res} .5416557
                        {txt}Largest       Std. dev.     {res} .2074917
{txt}75%    {res} .6821429       1.181818
{txt}90%    {res} .8166667       1.207547       {txt}Variance      {res} .0430528
{txt}95%    {res} .8984127       1.219512       {txt}Skewness      {res} .2292487
{txt}99%    {res} 1.053667       1.299107       {txt}Kurtosis      {res} 2.755596
{txt}
{com}. di r(p75)
{res}.68214285
{txt}
{com}. di r(p25)
{res}.39148939
{txt}
{com}. *
. gen tot_diffoccupseek_catD =.
{txt}(12,551 missing values generated)

{com}. replace tot_diffoccupseek_catD = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,055 real changes made)

{com}. replace tot_diffoccupseek_catD = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,951 real changes made)

{com}. replace tot_diffoccupseek_catD = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,545 real changes made)

{com}. *
. tab tot_diffoccupseek_catD if e(sample)

{txt}tot_diffocc {c |}
upseek_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,497       25.00       25.00
{txt}          1 {c |}{res}      2,993       49.99       75.00
{txt}          2 {c |}{res}      1,497       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,987      100.00
{txt}
{com}. tab tot_diffoccupseek_catD if e(sample) & itmod_adopt_state==1

{txt}tot_diffocc {c |}
upseek_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,497       25.00       25.00
{txt}          1 {c |}{res}      2,993       49.99       75.00
{txt}          2 {c |}{res}      1,497       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,987      100.00
{txt}
{com}. *
. *
. *
. 
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt   if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. *
. *
. sum t1_diffoccupseek if e(sample), detail

                      {txt}t1_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .0434783              0
{txt} 5%    {res} .0909091              0
{txt}10%    {res}     .125              0       {txt}Obs         {res}      6,016
{txt}25%    {res} .1842105              0       {txt}Sum of wgt. {res}      6,016

{txt}50%    {res}      .25                      {txt}Mean          {res} .2578286
                        {txt}Largest       Std. dev.     {res} .1058309
{txt}75%    {res}     .325       .6428571
{txt}90%    {res} .3947369       .6428571       {txt}Variance      {res} .0112002
{txt}95%    {res} .4444444       .7142857       {txt}Skewness      {res} .3254655
{txt}99%    {res} .5263158       .8214286       {txt}Kurtosis      {res} 3.137285
{txt}
{com}. di r(p75)
{res}.32499999
{txt}
{com}. di r(p25)
{res}.18421052
{txt}
{com}. *
. gen t1_diffoccupseek_catD =.
{txt}(12,551 missing values generated)

{com}. replace t1_diffoccupseek_catD = 0 if t1_diffoccupseek<= r(p25) 
{txt}(3,478 real changes made)

{com}. replace t1_diffoccupseek_catD = 1 if t1_diffoccupseek> r(p25) & t1_diffoccupseek < r(p75) 
{txt}(5,952 real changes made)

{com}. replace t1_diffoccupseek_catD = 2 if t1_diffoccupseek>= r(p75) 
{txt}(3,121 real changes made)

{com}. *
. tab t1_diffoccupseek_catD if e(sample)

{txt}t1_diffoccu {c |}
 pseek_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,506       25.03       25.03
{txt}          1 {c |}{res}      2,979       49.52       74.55
{txt}          2 {c |}{res}      1,531       25.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,016      100.00
{txt}
{com}. tab t1_diffoccupseek_catD if e(sample) & itmod_adopt_state==1

{txt}t1_diffoccu {c |}
 pseek_catD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,506       25.03       25.03
{txt}          1 {c |}{res}      2,979       49.52       74.55
{txt}          2 {c |}{res}      1,531       25.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,016      100.00
{txt}
{com}. *
. *
. *
. 
. * 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020
{txt}
{com}. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1351351       .0181818
{txt} 5%    {res} .2156028       .0277778
{txt}10%    {res} .2758985       .0527778       {txt}Obs         {res}      5,798
{txt}25%    {res} .3916667       .0548048       {txt}Sum of wgt. {res}      5,798

{txt}50%    {res} .5372086                      {txt}Mean          {res} .5431529
                        {txt}Largest       Std. dev.     {res} .2070711
{txt}75%    {res} .6833333       1.181818
{txt}90%    {res} .8189702       1.207547       {txt}Variance      {res} .0428785
{txt}95%    {res}       .9       1.219512       {txt}Skewness      {res} .2517858
{txt}99%    {res} 1.055556       1.299107       {txt}Kurtosis      {res} 2.720754
{txt}
{com}. di r(p75)
{res}.68333334
{txt}
{com}. di r(p25)
{res}.39166665
{txt}
{com}. *
. gen relt1_diffoccupseek_catD =.
{txt}(12,551 missing values generated)

{com}. replace relt1_diffoccupseek_catD = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,068 real changes made)

{com}. replace relt1_diffoccupseek_catD = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,955 real changes made)

{com}. replace relt1_diffoccupseek_catD = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,528 real changes made)

{com}. *
. tab relt1_diffoccupseek_catD if e(sample)

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
         tD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,454       25.08       25.08
{txt}          1 {c |}{res}      2,889       49.83       74.91
{txt}          2 {c |}{res}      1,455       25.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,798      100.00
{txt}
{com}. tab relt1_diffoccupseek_catD if e(sample) & itmod_adopt_state==1

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
         tD {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,454       25.08       25.08
{txt}          1 {c |}{res}      2,889       49.83       74.91
{txt}          2 {c |}{res}      1,455       25.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,798      100.00
{txt}
{com}. *
. *
. *
. 
. 
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *
. 
. 
. *** TESTING H1 & H3: TOTAL PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION ***
. 
. 
. 
. 
. *** ESTIMATE MODEL D1: TOTAL PROGRAM ERROR  RATE [MODEL D1  with 2002-2004 & 2020-2022 YEARS OMITTED FROM ESTIMATION SAMPLE: PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, and ADOPTION YEAR COHORT UNIT EFFECTS] ***       (FIGURES D1A-D1C) 
. 
. 
. npregress series totalerror_rat  itmod_monthcount i.tot_interstate_catD   i.tot_diffoccupseek_catD if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat   i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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t}820{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}830{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}840{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}850{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}860{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}870{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}880{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}890{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}900{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}910{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}920{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}930{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}940{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}950{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}960{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}970{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}980{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}990{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}1,000{text} done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:npregress}.
{res}
{txt}Cubic B-spline estimation {col 44}Number of obs      =  {res}        5,987
{txt}Criterion: {res:cross validation}{col 44}Number of knots    =  {res}            1
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}   Observed{col 40}   Bootstrap{col 68}         Norm{col 81}al-based
{col 1}            totalerror_rat{col 28}{c |}     effect{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}itmod_monthcount {c |}{col 28}{res}{space 2}-.0001564{col 40}{space 2} 7.195936{col 51}{space 1}   -0.00{col 60}{space 3}1.000{col 68}{space 4}-14.10393{col 81}{space 3} 14.10362
{txt}{space 26} {c |}
{space 7}tot_interstate_catD {c |}
{space 24}1  {c |}{col 28}{res}{space 2}-.0006204{col 40}{space 2} .0053346{col 51}{space 1}   -0.12{col 60}{space 3}0.907{col 68}{space 4} -.011076{col 81}{space 3} .0098353
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.0002957{col 40}{space 2} .0065303{col 51}{space 1}   -0.05{col 60}{space 3}0.964{col 68}{space 4}-.0130948{col 81}{space 3} .0125034
{txt}{space 26} {c |}
{space 4}tot_diffoccupseek_catD {c |}
{space 24}1  {c |}{col 28}{res}{space 2}  .012423{col 40}{space 2} .0101662{col 51}{space 1}    1.22{col 60}{space 3}0.222{col 68}{space 4}-.0075023{col 81}{space 3} .0323484
{txt}{space 24}2  {c |}{col 28}{res}{space 2}  .023901{col 40}{space 2} .0104932{col 51}{space 1}    2.28{col 60}{space 3}0.023{col 68}{space 4} .0033348{col 81}{space 3} .0444672
{txt}{space 26} {c |}
{space 15}demgovparty {c |}{col 28}{res}{space 2}-.0074899{col 40}{space 2} .0134228{col 51}{space 1}   -0.56{col 60}{space 3}0.577{col 68}{space 4}-.0337982{col 81}{space 3} .0188184
{txt}{space 15}repgovparty {c |}{col 28}{res}{space 2}-.0051939{col 40}{space 2} .0133544{col 51}{space 1}   -0.39{col 60}{space 3}0.697{col 68}{space 4} -.031368{col 81}{space 3} .0209803
{txt}{space 15}ln_workload {c |}{col 28}{res}{space 2} .0137219{col 40}{space 2} .0036909{col 51}{space 1}    3.72{col 60}{space 3}0.000{col 68}{space 4} .0064879{col 81}{space 3} .0209559
{txt}{space 12}automationrate {c |}{col 28}{res}{space 2}  .084383{col 40}{space 2} .0102627{col 51}{space 1}    8.22{col 60}{space 3}0.000{col 68}{space 4} .0642686{col 81}{space 3} .1044974
{txt}{space 4}ln_uiadmin_budget_real {c |}{col 28}{res}{space 2} .0635953{col 40}{space 2} .0120936{col 51}{space 1}    5.26{col 60}{space 3}0.000{col 68}{space 4} .0398923{col 81}{space 3} .0872984
{txt}{space 8}benefitgenerosity2 {c |}{col 28}{res}{space 2} .0598576{col 40}{space 2} .0208688{col 51}{space 1}    2.87{col 60}{space 3}0.004{col 68}{space 4} .0189555{col 81}{space 3} .1007597
{txt}{space 16}unemp_rate {c |}{col 28}{res}{space 2} .0036993{col 40}{space 2} .0015772{col 51}{space 1}    2.35{col 60}{space 3}0.019{col 68}{space 4} .0006079{col 81}{space 3} .0067906
{txt}ln_function_sup_avgsalreal {c |}{col 28}{res}{space 2} .0297652{col 40}{space 2} .0126565{col 51}{space 1}    2.35{col 60}{space 3}0.019{col 68}{space 4} .0049589{col 81}{space 3} .0545715
{txt}{space 5}tot_totalnonwhite_rat {c |}{col 28}{res}{space 2} .0550703{col 40}{space 2} .0078674{col 51}{space 1}    7.00{col 60}{space 3}0.000{col 68}{space 4} .0396504{col 81}{space 3} .0704903
{txt}{space 7}tot_totalfemale_rat {c |}{col 28}{res}{space 2} .0046743{col 40}{space 2} .0095257{col 51}{space 1}    0.49{col 60}{space 3}0.624{col 68}{space 4}-.0139957{col 81}{space 3} .0233443
{txt}{space 4}tot_totalageu25o65_rat {c |}{col 28}{res}{space 2}-.0113845{col 40}{space 2} .0134689{col 51}{space 1}   -0.85{col 60}{space 3}0.398{col 68}{space 4} -.037783{col 81}{space 3} .0150141
{txt}{space 26} {c |}
{space 19}stateid {c |}
{space 24}5  {c |}{col 28}{res}{space 2}-.1758981{col 40}{space 2} .0326989{col 51}{space 1}   -5.38{col 60}{space 3}0.000{col 68}{space 4}-.2399867{col 81}{space 3}-.1118095
{txt}{space 24}6  {c |}{col 28}{res}{space 2}-.0072955{col 40}{space 2} .0128194{col 51}{space 1}   -0.57{col 60}{space 3}0.569{col 68}{space 4} -.032421{col 81}{space 3}   .01783
{txt}{space 24}9  {c |}{col 28}{res}{space 2}-.1254284{col 40}{space 2} .0159411{col 51}{space 1}   -7.87{col 60}{space 3}0.000{col 68}{space 4}-.1566724{col 81}{space 3}-.0941843
{txt}{space 23}12  {c |}{col 28}{res}{space 2} .0411667{col 40}{space 2} .0137139{col 51}{space 1}    3.00{col 60}{space 3}0.003{col 68}{space 4} .0142879{col 81}{space 3} .0680455
{txt}{space 23}13  {c |}{col 28}{res}{space 2} .0143897{col 40}{space 2} .0211054{col 51}{space 1}    0.68{col 60}{space 3}0.495{col 68}{space 4}-.0269761{col 81}{space 3} .0557555
{txt}{space 23}14  {c |}{col 28}{res}{space 2} .2368067{col 40}{space 2} .0196612{col 51}{space 1}   12.04{col 60}{space 3}0.000{col 68}{space 4} .1982713{col 81}{space 3}  .275342
{txt}{space 23}18  {c |}{col 28}{res}{space 2} .2123926{col 40}{space 2} .0157471{col 51}{space 1}   13.49{col 60}{space 3}0.000{col 68}{space 4} .1815288{col 81}{space 3} .2432564
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .2292961{col 40}{space 2} .0145431{col 51}{space 1}   15.77{col 60}{space 3}0.000{col 68}{space 4} .2007921{col 81}{space 3} .2578001
{txt}{space 23}20  {c |}{col 28}{res}{space 2}  .010139{col 40}{space 2} .0108954{col 51}{space 1}    0.93{col 60}{space 3}0.352{col 68}{space 4}-.0112157{col 81}{space 3} .0314937
{txt}{space 23}21  {c |}{col 28}{res}{space 2}  .141699{col 40}{space 2} .0132241{col 51}{space 1}   10.72{col 60}{space 3}0.000{col 68}{space 4} .1157802{col 81}{space 3} .1676178
{txt}{space 23}22  {c |}{col 28}{res}{space 2}-.0564296{col 40}{space 2} .0192954{col 51}{space 1}   -2.92{col 60}{space 3}0.003{col 68}{space 4}-.0942479{col 81}{space 3}-.0186113
{txt}{space 23}23  {c |}{col 28}{res}{space 2} .0696265{col 40}{space 2} .0126289{col 51}{space 1}    5.51{col 60}{space 3}0.000{col 68}{space 4} .0448743{col 81}{space 3} .0943787
{txt}{space 23}24  {c |}{col 28}{res}{space 2} -.067619{col 40}{space 2} .0110064{col 51}{space 1}   -6.14{col 60}{space 3}0.000{col 68}{space 4}-.0891912{col 81}{space 3}-.0460469
{txt}{space 23}25  {c |}{col 28}{res}{space 2} .0247615{col 40}{space 2}  .009889{col 51}{space 1}    2.50{col 60}{space 3}0.012{col 68}{space 4} .0053794{col 81}{space 3} .0441436
{txt}{space 23}27  {c |}{col 28}{res}{space 2} .2041048{col 40}{space 2} .0147517{col 51}{space 1}   13.84{col 60}{space 3}0.000{col 68}{space 4} .1751919{col 81}{space 3} .2330177
{txt}{space 23}28  {c |}{col 28}{res}{space 2} .0519012{col 40}{space 2}  .008635{col 51}{space 1}    6.01{col 60}{space 3}0.000{col 68}{space 4} .0349768{col 81}{space 3} .0688255
{txt}{space 23}29  {c |}{col 28}{res}{space 2} .2315715{col 40}{space 2}  .017601{col 51}{space 1}   13.16{col 60}{space 3}0.000{col 68}{space 4} .1970741{col 81}{space 3} .2660688
{txt}{space 23}31  {c |}{col 28}{res}{space 2} .0947857{col 40}{space 2} .0300698{col 51}{space 1}    3.15{col 60}{space 3}0.002{col 68}{space 4}   .03585{col 81}{space 3} .1537214
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0049142{col 40}{space 2} .0110112{col 51}{space 1}   -0.45{col 60}{space 3}0.655{col 68}{space 4}-.0264958{col 81}{space 3} .0166674
{txt}{space 23}35  {c |}{col 28}{res}{space 2} .0453696{col 40}{space 2} .0241835{col 51}{space 1}    1.88{col 60}{space 3}0.061{col 68}{space 4}-.0020292{col 81}{space 3} .0927684
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .1516797{col 40}{space 2} .0234912{col 51}{space 1}    6.46{col 60}{space 3}0.000{col 68}{space 4} .1056378{col 81}{space 3} .1977215
{txt}{space 23}40  {c |}{col 28}{res}{space 2} .0124648{col 40}{space 2} .0106164{col 51}{space 1}    1.17{col 60}{space 3}0.240{col 68}{space 4} -.008343{col 81}{space 3} .0332725
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .1522379{col 40}{space 2} .0098366{col 51}{space 1}   15.48{col 60}{space 3}0.000{col 68}{space 4} .1329585{col 81}{space 3} .1715173
{txt}{space 23}44  {c |}{col 28}{res}{space 2} .0912237{col 40}{space 2}  .014275{col 51}{space 1}    6.39{col 60}{space 3}0.000{col 68}{space 4} .0632453{col 81}{space 3} .1192021
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0543627{col 40}{space 2} .0111572{col 51}{space 1}    4.87{col 60}{space 3}0.000{col 68}{space 4} .0324951{col 81}{space 3} .0762304
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0296113{col 40}{space 2}  .015219{col 51}{space 1}   -1.95{col 60}{space 3}0.052{col 68}{space 4}  -.05944{col 81}{space 3} .0002174
{txt}{space 23}50  {c |}{col 28}{res}{space 2}  .178989{col 40}{space 2} .0228526{col 51}{space 1}    7.83{col 60}{space 3}0.000{col 68}{space 4} .1341987{col 81}{space 3} .2237793
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2}-.0078522{col 40}{space 2} .0074551{col 51}{space 1}   -1.05{col 60}{space 3}0.292{col 68}{space 4}-.0224639{col 81}{space 3} .0067595
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0023499{col 40}{space 2} .0073319{col 51}{space 1}   -0.32{col 60}{space 3}0.749{col 68}{space 4}-.0167202{col 81}{space 3} .0120203
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0033445{col 40}{space 2} .0083586{col 51}{space 1}   -0.40{col 60}{space 3}0.689{col 68}{space 4}-.0197271{col 81}{space 3}  .013038
{txt}{space 21}2006  {c |}{col 28}{res}{space 2} .0069951{col 40}{space 2} .0083917{col 51}{space 1}    0.83{col 60}{space 3}0.405{col 68}{space 4}-.0094523{col 81}{space 3} .0234425
{txt}{space 21}2007  {c |}{col 28}{res}{space 2} .0129671{col 40}{space 2} .0085659{col 51}{space 1}    1.51{col 60}{space 3}0.130{col 68}{space 4}-.0038218{col 81}{space 3} .0297559
{txt}{space 21}2008  {c |}{col 28}{res}{space 2} .0054935{col 40}{space 2} .0083891{col 51}{space 1}    0.65{col 60}{space 3}0.513{col 68}{space 4}-.0109489{col 81}{space 3} .0219358
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.0044128{col 40}{space 2} .0106938{col 51}{space 1}   -0.41{col 60}{space 3}0.680{col 68}{space 4}-.0253723{col 81}{space 3} .0165468
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0362384{col 40}{space 2} .0117067{col 51}{space 1}    3.10{col 60}{space 3}0.002{col 68}{space 4} .0132938{col 81}{space 3} .0591831
{txt}{space 21}2011  {c |}{col 28}{res}{space 2} .0211052{col 40}{space 2} .0102958{col 51}{space 1}    2.05{col 60}{space 3}0.040{col 68}{space 4} .0009257{col 81}{space 3} .0412846
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0080359{col 40}{space 2} .0101395{col 51}{space 1}    0.79{col 60}{space 3}0.428{col 68}{space 4}-.0118373{col 81}{space 3}  .027909
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} .0121555{col 40}{space 2} .0109348{col 51}{space 1}    1.11{col 60}{space 3}0.266{col 68}{space 4}-.0092763{col 81}{space 3} .0335874
{txt}{space 21}2014  {c |}{col 28}{res}{space 2} .0282831{col 40}{space 2} .0114451{col 51}{space 1}    2.47{col 60}{space 3}0.013{col 68}{space 4} .0058512{col 81}{space 3}  .050715
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .0223876{col 40}{space 2} .0118991{col 51}{space 1}    1.88{col 60}{space 3}0.060{col 68}{space 4}-.0009343{col 81}{space 3} .0457095
{txt}{space 21}2016  {c |}{col 28}{res}{space 2} .0300447{col 40}{space 2} .0113873{col 51}{space 1}    2.64{col 60}{space 3}0.008{col 68}{space 4} .0077259{col 81}{space 3} .0523635
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0456569{col 40}{space 2} .0119867{col 51}{space 1}    3.81{col 60}{space 3}0.000{col 68}{space 4} .0221635{col 81}{space 3} .0691503
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .0471577{col 40}{space 2} .0132602{col 51}{space 1}    3.56{col 60}{space 3}0.000{col 68}{space 4} .0211681{col 81}{space 3} .0731472
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0266928{col 40}{space 2} .0123075{col 51}{space 1}    2.17{col 60}{space 3}0.030{col 68}{space 4} .0025705{col 81}{space 3} .0508152
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .1452172{col 40}{space 2} .0334534{col 51}{space 1}    4.34{col 60}{space 3}0.000{col 68}{space 4} .0796497{col 81}{space 3} .2107848
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .0380589{col 40}{space 2}  .023819{col 51}{space 1}    1.60{col 60}{space 3}0.110{col 68}{space 4}-.0086255{col 81}{space 3} .0847433
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .0015717{col 40}{space 2}  .017805{col 51}{space 1}    0.09{col 60}{space 3}0.930{col 68}{space 4}-.0333255{col 81}{space 3} .0364689
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2}-.0047743{col 40}{space 2} .0164923{col 51}{space 1}   -0.29{col 60}{space 3}0.772{col 68}{space 4}-.0370986{col 81}{space 3}   .02755
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2} .1240165{col 40}{space 2} .0171022{col 51}{space 1}    7.25{col 60}{space 3}0.000{col 68}{space 4} .0904968{col 81}{space 3} .1575362
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2} .0272344{col 40}{space 2} .0187968{col 51}{space 1}    1.45{col 60}{space 3}0.147{col 68}{space 4}-.0096065{col 81}{space 3} .0640754
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2}-.0046367{col 40}{space 2} .0149348{col 51}{space 1}   -0.31{col 60}{space 3}0.756{col 68}{space 4}-.0339084{col 81}{space 3}  .024635
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2}-.1006952{col 40}{space 2} .0287389{col 51}{space 1}   -3.50{col 60}{space 3}0.000{col 68}{space 4}-.1570223{col 81}{space 3}-.0443681
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0566242{col 40}{space 2} .0203767{col 51}{space 1}   -2.78{col 60}{space 3}0.005{col 68}{space 4}-.0965618{col 81}{space 3}-.0166867
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2} -.098422{col 40}{space 2} .0181222{col 51}{space 1}   -5.43{col 60}{space 3}0.000{col 68}{space 4}-.1339407{col 81}{space 3}-.0629032
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2} .0056958{col 40}{space 2} .0187885{col 51}{space 1}    0.30{col 60}{space 3}0.762{col 68}{space 4} -.031129{col 81}{space 3} .0425206
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0314039{col 40}{space 2} .0191822{col 51}{space 1}   -1.64{col 60}{space 3}0.102{col 68}{space 4}-.0690003{col 81}{space 3} .0061924
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m1d if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m1d if e(sample), residuals
{res}{txt}(6,564 missing values generated)

{com}. 
. gen sse_m1d = predsy_m1d * predsy_m1d if e(sample)
{txt}(6,564 missing values generated)

{com}. gen ssr_m1d = residsy_m1d * residsy_m1d if e(sample)
{txt}(6,564 missing values generated)

{com}. 
. egen sum_sse_m1d = total(sse_m1d) if e(sample)
{txt}(6,564 missing values generated)

{com}. egen sum_ssr_m1d = total(ssr_m1d) if e(sample)
{txt}(6,564 missing values generated)

{com}. 
. gen r2_m1d = sum_ssr_m1d/(sum_sse_m1d + sum_ssr_m1d)
{txt}(6,564 missing values generated)

{com}. 
. sum r2_m1d

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m1d {c |}{res}      5,987    .2026141           0   .2026141   .2026141
{txt}
{com}. 
. *
. *
. *
. 
. * [MODEL D1: TOTAL PROGRAM ERROR RATE] FIGURE D1A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:5,987}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1761356{col 26}{space 2} .0031479{col 37}{space 1}   55.95{col 46}{space 3}0.000{col 54}{space 4} .1699659{col 67}{space 3} .1823054
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1761541{col 26}{space 2} .0028082{col 37}{space 1}   62.73{col 46}{space 3}0.000{col 54}{space 4} .1706501{col 67}{space 3} .1816581
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .176051{col 26}{space 2} .0017908{col 37}{space 1}   98.31{col 46}{space 3}0.000{col 54}{space 4} .1725411{col 67}{space 3} .1795609
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .175506{col 26}{space 2} .0025019{col 37}{space 1}   70.15{col 46}{space 3}0.000{col 54}{space 4} .1706025{col 67}{space 3} .1804096
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1745152{col 26}{space 2} .0038341{col 37}{space 1}   45.52{col 46}{space 3}0.000{col 54}{space 4} .1670005{col 67}{space 3} .1820299
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1730929{col 26}{space 2} .0050413{col 37}{space 1}   34.33{col 46}{space 3}0.000{col 54}{space 4} .1632121{col 67}{space 3} .1829737
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1712538{col 26}{space 2} .0060253{col 37}{space 1}   28.42{col 46}{space 3}0.000{col 54}{space 4} .1594444{col 67}{space 3} .1830632
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1690123{col 26}{space 2} .0067881{col 37}{space 1}   24.90{col 46}{space 3}0.000{col 54}{space 4} .1557079{col 67}{space 3} .1823168
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .166383{col 26}{space 2} .0073529{col 37}{space 1}   22.63{col 46}{space 3}0.000{col 54}{space 4} .1519715{col 67}{space 3} .1807945
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1633802{col 26}{space 2} .0077492{col 37}{space 1}   21.08{col 46}{space 3}0.000{col 54}{space 4}  .148192{col 67}{space 3} .1785685
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1600187{col 26}{space 2} .0080083{col 37}{space 1}   19.98{col 46}{space 3}0.000{col 54}{space 4} .1443227{col 67}{space 3} .1757147
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1563127{col 26}{space 2} .0081614{col 37}{space 1}   19.15{col 46}{space 3}0.000{col 54}{space 4} .1403167{col 67}{space 3} .1723088
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE D1A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Total Program Error Rate [MODEL D1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1A.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. * [MODEL D1: TOTAL PROGRAM ERROR RATE] FIGURE D1B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_catD==2) & LOW COMPLEXITY (tot_interstate_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_interstate_catD if tot_interstate_catD==0|tot_interstate_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,035}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 24}{c TT}{hline 11}{hline 12}{hline 11}
{col 25}{text}{c |}         df{col 37}        chi2{col 49}     P>chi2
{res}{col 1}{text}{hline 24}{c +}{hline 11}{hline 12}{hline 11}
tot_interstate_catD@_at {c |}
{space 11}(2 vs 0)  1  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.04{col 49}{space 2}   0.8320
{txt}{space 11}(2 vs 0)  2  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.08{col 49}{space 2}   0.7817
{txt}{space 11}(2 vs 0)  3  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.23{col 49}{space 2}   0.6317
{txt}{space 11}(2 vs 0)  4  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.19{col 49}{space 2}   0.6644
{txt}{space 11}(2 vs 0)  5  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.06{col 49}{space 2}   0.8138
{txt}{space 11}(2 vs 0)  6  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.00{col 49}{space 2}   0.9799
{txt}{space 11}(2 vs 0)  7  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.11{col 49}{space 2}   0.7417
{txt}{space 11}(2 vs 0)  8  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.46{col 49}{space 2}   0.4993
{txt}{space 11}(2 vs 0)  9  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.13{col 49}{space 2}   0.2886
{txt}{space 11}(2 vs 0) 10  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     2.18{col 49}{space 2}   0.1398
{txt}{space 11}(2 vs 0) 11  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     3.61{col 49}{space 2}   0.0573
{txt}{space 11}(2 vs 0) 12  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     5.30{col 49}{space 2}   0.0213
{col 1}{text}                 Joint {col 25}{c |}{result}  (not testable)
{col 1}{text}{hline 24}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 25}{c |}{col 37} Delta-method
{col 25}{c |}   Contrast{col 37}   std. err.{col 49}     [95% con{col 62}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_interstate_catD@_at {c |}
{space 11}(2 vs 0)  1  {c |}{col 25}{res}{space 2} .0012898{col 37}{space 2} .0060814{col 48}{space 5}-.0106295{col 62}{space 3} .0132092
{txt}{space 11}(2 vs 0)  2  {c |}{col 25}{res}{space 2} .0016306{col 37}{space 2} .0058857{col 48}{space 5}-.0099052{col 62}{space 3} .0131664
{txt}{space 11}(2 vs 0)  3  {c |}{col 25}{res}{space 2}  .002733{col 37}{space 2} .0057025{col 48}{space 5}-.0084436{col 62}{space 3} .0139097
{txt}{space 11}(2 vs 0)  4  {c |}{col 25}{res}{space 2} .0028429{col 37}{space 2} .0065527{col 48}{space 5}-.0100002{col 62}{space 3} .0156861
{txt}{space 11}(2 vs 0)  5  {c |}{col 25}{res}{space 2} .0017999{col 37}{space 2} .0076421{col 48}{space 5}-.0131784{col 62}{space 3} .0167781
{txt}{space 11}(2 vs 0)  6  {c |}{col 25}{res}{space 2}-.0002158{col 37}{space 2} .0085489{col 48}{space 5}-.0169714{col 62}{space 3} .0165398
{txt}{space 11}(2 vs 0)  7  {c |}{col 25}{res}{space 2}-.0030237{col 37}{space 2} .0091744{col 48}{space 5}-.0210051{col 62}{space 3} .0149577
{txt}{space 11}(2 vs 0)  8  {c |}{col 25}{res}{space 2}-.0064435{col 37}{space 2} .0095373{col 48}{space 5}-.0251362{col 62}{space 3} .0122493
{txt}{space 11}(2 vs 0)  9  {c |}{col 25}{res}{space 2}-.0102947{col 37}{space 2} .0097019{col 48}{space 5}-.0293101{col 62}{space 3} .0087207
{txt}{space 11}(2 vs 0) 10  {c |}{col 25}{res}{space 2}-.0143971{col 37}{space 2} .0097503{col 48}{space 5}-.0335073{col 62}{space 3} .0047131
{txt}{space 11}(2 vs 0) 11  {c |}{col 25}{res}{space 2}-.0185703{col 37}{space 2} .0097674{col 48}{space 5}-.0377141{col 62}{space 3} .0005736
{txt}{space 11}(2 vs 0) 12  {c |}{col 25}{res}{space 2}-.0226338{col 37}{space 2} .0098281{col 48}{space 5}-.0418965{col 62}{space 3}-.0033711
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE D1B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Total Program Error Rate [MODEL D1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1B.04-10-2025.gph} saved

{com}. *
. *
. *
. * [MODEL D1: TOTAL PROGRAM ERROR RATE] FIGURE D1C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_catD==2) & LOW COMPLEXITY (tot_extbenefits_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. margins r.tot_diffoccupseek_catD if tot_diffoccupseek_catD==0|tot_diffoccupseek_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,994}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
tot_diffoccupseek_catD@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    39.16{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    42.68{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    39.72{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    22.44{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    13.32{col 52}{space 2}   0.0003
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.73{col 52}{space 2}   0.0031
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     6.03{col 52}{space 2}   0.0140
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     4.20{col 52}{space 2}   0.0405
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     2.80{col 52}{space 2}   0.0945
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     1.67{col 52}{space 2}   0.1969
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.78{col 52}{space 2}   0.3765
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.20{col 52}{space 2}   0.6514
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_diffoccupseek_catD@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2} .0312868{col 40}{space 2} .0049999{col 51}{space 5} .0214872{col 65}{space 3} .0410864
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2} .0313649{col 40}{space 2} .0048011{col 51}{space 5}  .021955{col 65}{space 3} .0407749
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2} .0313677{col 40}{space 2} .0049772{col 51}{space 5} .0216126{col 65}{space 3} .0411227
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2} .0305759{col 40}{space 2} .0064549{col 51}{space 5} .0179245{col 65}{space 3} .0432273
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2}  .029008{col 40}{space 2} .0079482{col 51}{space 5} .0134298{col 65}{space 3} .0445863
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2} .0267605{col 40}{space 2} .0090575{col 51}{space 5} .0090081{col 65}{space 3} .0445129
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2} .0239298{col 40}{space 2} .0097428{col 51}{space 5} .0048344{col 65}{space 3} .0430253
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2} .0206126{col 40}{space 2} .0100624{col 51}{space 5} .0008906{col 65}{space 3} .0403345
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2} .0169052{col 40}{space 2} .0101108{col 51}{space 5}-.0029116{col 65}{space 3}  .036722
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2} .0129042{col 40}{space 2}  .009999{col 51}{space 5}-.0066936{col 65}{space 3}  .032502
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2} .0087061{col 40}{space 2} .0098441{col 51}{space 5}-.0105879{col 65}{space 3} .0280002
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2} .0044074{col 40}{space 2} .0097547{col 51}{space 5}-.0147114{col 65}{space 3} .0235262
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE D1C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Total Program Error Rate [MODEL D1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D1.FIGURE D1C.04-10-2025.gph} saved

{com}. *
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** TESTING H2 & H4: ABSOLUTE TYPE I ERROR RATE ORGANZATIONAL ADAPTATION  ***
. 
. 
. 
. *** ESTIMATE MODEL D2: ABSOLUTE TYPE I ERROR RATE [MODEL 2 with 2002-2004 & 2020-2022 YEARS OMITTED FROM ESTIMATION SAMPLE: PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [PLUS STATE, YEAR, and ADOPTION YEAR COHORT UNIT EFFECTS] ***  (FIGURES D2A-D2C) 
. 
. 
. npregress series t1error_rat  itmod_monthcount  i.t1_interstate_catD   i.t1_diffoccupseek_catD if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate  ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat   i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}29  {c |}{col 28}{res}{space 2} .0091682{col 40}{space 2} .0100627{col 51}{space 1}    0.91{col 60}{space 3}0.362{col 68}{space 4}-.0105543{col 81}{space 3} .0288906
{txt}{space 23}31  {c |}{col 28}{res}{space 2} .0035889{col 40}{space 2} .0115644{col 51}{space 1}    0.31{col 60}{space 3}0.756{col 68}{space 4}-.0190769{col 81}{space 3} .0262546
{txt}{space 23}33  {c |}{col 28}{res}{space 2} .0085458{col 40}{space 2} .0073161{col 51}{space 1}    1.17{col 60}{space 3}0.243{col 68}{space 4}-.0057935{col 81}{space 3}  .022885
{txt}{space 23}35  {c |}{col 28}{res}{space 2} .0131284{col 40}{space 2} .0149005{col 51}{space 1}    0.88{col 60}{space 3}0.378{col 68}{space 4}-.0160761{col 81}{space 3} .0423329
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .1318465{col 40}{space 2} .0187161{col 51}{space 1}    7.04{col 60}{space 3}0.000{col 68}{space 4} .0951636{col 81}{space 3} .1685294
{txt}{space 23}40  {c |}{col 28}{res}{space 2}-.0054602{col 40}{space 2} .0065718{col 51}{space 1}   -0.83{col 60}{space 3}0.406{col 68}{space 4}-.0183407{col 81}{space 3} .0074203
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .0282523{col 40}{space 2} .0057999{col 51}{space 1}    4.87{col 60}{space 3}0.000{col 68}{space 4} .0168846{col 81}{space 3} .0396199
{txt}{space 23}44  {c |}{col 28}{res}{space 2} .0196794{col 40}{space 2} .0092432{col 51}{space 1}    2.13{col 60}{space 3}0.033{col 68}{space 4} .0015631{col 81}{space 3} .0377957
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0192752{col 40}{space 2} .0070189{col 51}{space 1}    2.75{col 60}{space 3}0.006{col 68}{space 4} .0055184{col 81}{space 3} .0330319
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0131428{col 40}{space 2} .0099888{col 51}{space 1}   -1.32{col 60}{space 3}0.188{col 68}{space 4}-.0327205{col 81}{space 3}  .006435
{txt}{space 23}50  {c |}{col 28}{res}{space 2} .0417301{col 40}{space 2} .0142422{col 51}{space 1}    2.93{col 60}{space 3}0.003{col 68}{space 4}  .013816{col 81}{space 3} .0696443
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} -.008445{col 40}{space 2} .0044267{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4}-.0171212{col 81}{space 3} .0002313
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0118735{col 40}{space 2}  .004572{col 51}{space 1}   -2.60{col 60}{space 3}0.009{col 68}{space 4}-.0208345{col 81}{space 3}-.0029124
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0132364{col 40}{space 2} .0046046{col 51}{space 1}   -2.87{col 60}{space 3}0.004{col 68}{space 4}-.0222612{col 81}{space 3}-.0042117
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0118941{col 40}{space 2} .0050385{col 51}{space 1}   -2.36{col 60}{space 3}0.018{col 68}{space 4}-.0217694{col 81}{space 3}-.0020188
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0106501{col 40}{space 2} .0050157{col 51}{space 1}   -2.12{col 60}{space 3}0.034{col 68}{space 4}-.0204806{col 81}{space 3}-.0008196
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0099751{col 40}{space 2} .0053827{col 51}{space 1}   -1.85{col 60}{space 3}0.064{col 68}{space 4}-.0205249{col 81}{space 3} .0005748
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.0057502{col 40}{space 2} .0068517{col 51}{space 1}   -0.84{col 60}{space 3}0.401{col 68}{space 4}-.0191792{col 81}{space 3} .0076789
{txt}{space 21}2010  {c |}{col 28}{res}{space 2}  .014176{col 40}{space 2} .0073532{col 51}{space 1}    1.93{col 60}{space 3}0.054{col 68}{space 4}-.0002361{col 81}{space 3}  .028588
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}-.0025184{col 40}{space 2} .0065491{col 51}{space 1}   -0.38{col 60}{space 3}0.701{col 68}{space 4}-.0153543{col 81}{space 3} .0103176
{txt}{space 21}2012  {c |}{col 28}{res}{space 2}-.0039567{col 40}{space 2} .0065933{col 51}{space 1}   -0.60{col 60}{space 3}0.548{col 68}{space 4}-.0168794{col 81}{space 3} .0089659
{txt}{space 21}2013  {c |}{col 28}{res}{space 2}-.0163762{col 40}{space 2} .0065085{col 51}{space 1}   -2.52{col 60}{space 3}0.012{col 68}{space 4}-.0291328{col 81}{space 3}-.0036197
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.0045693{col 40}{space 2} .0073715{col 51}{space 1}   -0.62{col 60}{space 3}0.535{col 68}{space 4}-.0190172{col 81}{space 3} .0098786
{txt}{space 21}2015  {c |}{col 28}{res}{space 2}  .000574{col 40}{space 2} .0079193{col 51}{space 1}    0.07{col 60}{space 3}0.942{col 68}{space 4}-.0149477{col 81}{space 3} .0160956
{txt}{space 21}2016  {c |}{col 28}{res}{space 2} .0052546{col 40}{space 2} .0078645{col 51}{space 1}    0.67{col 60}{space 3}0.504{col 68}{space 4}-.0101596{col 81}{space 3} .0206688
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0132999{col 40}{space 2} .0079853{col 51}{space 1}    1.67{col 60}{space 3}0.096{col 68}{space 4}-.0023509{col 81}{space 3} .0289508
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .0139379{col 40}{space 2} .0094064{col 51}{space 1}    1.48{col 60}{space 3}0.138{col 68}{space 4}-.0044982{col 81}{space 3}  .032374
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0075963{col 40}{space 2} .0087247{col 51}{space 1}    0.87{col 60}{space 3}0.384{col 68}{space 4}-.0095037{col 81}{space 3} .0246963
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .0875444{col 40}{space 2} .0113041{col 51}{space 1}    7.74{col 60}{space 3}0.000{col 68}{space 4} .0653888{col 81}{space 3} .1096999
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .0467361{col 40}{space 2} .0120331{col 51}{space 1}    3.88{col 60}{space 3}0.000{col 68}{space 4} .0231517{col 81}{space 3} .0703206
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .0076012{col 40}{space 2}   .00807{col 51}{space 1}    0.94{col 60}{space 3}0.346{col 68}{space 4}-.0082157{col 81}{space 3} .0234181
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .0033422{col 40}{space 2}   .00713{col 51}{space 1}    0.47{col 60}{space 3}0.639{col 68}{space 4}-.0106323{col 81}{space 3} .0173167
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2}-.0015009{col 40}{space 2} .0059194{col 51}{space 1}   -0.25{col 60}{space 3}0.800{col 68}{space 4}-.0131028{col 81}{space 3}  .010101
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2}-.0237098{col 40}{space 2} .0065264{col 51}{space 1}   -3.63{col 60}{space 3}0.000{col 68}{space 4}-.0365014{col 81}{space 3}-.0109182
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2}-.0111011{col 40}{space 2} .0058241{col 51}{space 1}   -1.91{col 60}{space 3}0.057{col 68}{space 4}-.0225161{col 81}{space 3} .0003139
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2}-.0589221{col 40}{space 2} .0171904{col 51}{space 1}   -3.43{col 60}{space 3}0.001{col 68}{space 4}-.0926146{col 81}{space 3}-.0252295
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0696974{col 40}{space 2} .0103411{col 51}{space 1}   -6.74{col 60}{space 3}0.000{col 68}{space 4}-.0899655{col 81}{space 3}-.0494293
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2}-.0468305{col 40}{space 2} .0070068{col 51}{space 1}   -6.68{col 60}{space 3}0.000{col 68}{space 4}-.0605636{col 81}{space 3}-.0330973
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0355877{col 40}{space 2} .0065796{col 51}{space 1}   -5.41{col 60}{space 3}0.000{col 68}{space 4}-.0484834{col 81}{space 3} -.022692
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0456012{col 40}{space 2}  .008468{col 51}{space 1}   -5.39{col 60}{space 3}0.000{col 68}{space 4}-.0621981{col 81}{space 3}-.0290042
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m2d if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m2d if e(sample), residuals
{res}{txt}(6,535 missing values generated)

{com}. 
. gen sse_m2d = predsy_m2d * predsy_m2d if e(sample)
{txt}(6,535 missing values generated)

{com}. gen ssr_m2d = residsy_m2d * residsy_m2d if e(sample)
{txt}(6,535 missing values generated)

{com}. 
. egen sum_sse_m2d = total(sse_m2d) if e(sample)
{txt}(6,535 missing values generated)

{com}. egen sum_ssr_m2d = total(ssr_m2d) if e(sample)
{txt}(6,535 missing values generated)

{com}. 
. gen r2_m2d = sum_ssr_m2d/(sum_sse_m2d + sum_ssr_m2d)
{txt}(6,535 missing values generated)

{com}. 
. sum r2_m2d

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m2d {c |}{res}      6,016    .4548227           0   .4548227   .4548227
{txt}
{com}. 
. *
. *
. *
. 
. * [MODEL D2: ABSOLUTE TYPE I ERROR RATE] FIGURE D2A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,016}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0580865{col 26}{space 2}  .001831{col 37}{space 1}   31.72{col 46}{space 3}0.000{col 54}{space 4} .0544978{col 67}{space 3} .0616752
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0577531{col 26}{space 2} .0016808{col 37}{space 1}   34.36{col 46}{space 3}0.000{col 54}{space 4} .0544589{col 67}{space 3} .0610474
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0560631{col 26}{space 2} .0012206{col 37}{space 1}   45.93{col 46}{space 3}0.000{col 54}{space 4} .0536708{col 67}{space 3} .0584555
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0539809{col 26}{space 2} .0014224{col 37}{space 1}   37.95{col 46}{space 3}0.000{col 54}{space 4}  .051193{col 67}{space 3} .0567688
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0518349{col 26}{space 2} .0019915{col 37}{space 1}   26.03{col 46}{space 3}0.000{col 54}{space 4} .0479317{col 67}{space 3} .0557382
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0496202{col 26}{space 2} .0025675{col 37}{space 1}   19.33{col 46}{space 3}0.000{col 54}{space 4}  .044588{col 67}{space 3} .0546524
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0473317{col 26}{space 2} .0030648{col 37}{space 1}   15.44{col 46}{space 3}0.000{col 54}{space 4} .0413247{col 67}{space 3} .0533387
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0449646{col 26}{space 2} .0034698{col 37}{space 1}   12.96{col 46}{space 3}0.000{col 54}{space 4} .0381638{col 67}{space 3} .0517653
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0425137{col 26}{space 2} .0037874{col 37}{space 1}   11.23{col 46}{space 3}0.000{col 54}{space 4} .0350906{col 67}{space 3} .0499368
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0399743{col 26}{space 2} .0040286{col 37}{space 1}    9.92{col 46}{space 3}0.000{col 54}{space 4} .0320784{col 67}{space 3} .0478701
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0373412{col 26}{space 2} .0042071{col 37}{space 1}    8.88{col 46}{space 3}0.000{col 54}{space 4} .0290954{col 67}{space 3}  .045587
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0346096{col 26}{space 2} .0043377{col 37}{space 1}    7.98{col 46}{space 3}0.000{col 54}{space 4} .0261078{col 67}{space 3} .0431114
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE D2A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Absolute Type I Program Error Rate [MODEL D2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2A.04-10-2025.gph} saved

{com}. 
. 
. 
. * [MODEL D2: ABSOLUTE TYPE I ERROR RATE] FIGURE D2B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_catD==2) & LOW COMPLEXITY (t1_interstate_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_interstate_catD if t1_interstate_catD==0|t1_interstate_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,022}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 23}{c TT}{hline 11}{hline 12}{hline 11}
{col 24}{text}{c |}         df{col 36}        chi2{col 48}     P>chi2
{res}{col 1}{text}{hline 23}{c +}{hline 11}{hline 12}{hline 11}
t1_interstate_catD@_at {c |}
{space 10}(2 vs 0)  1  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.25{col 48}{space 2}   0.6143
{txt}{space 10}(2 vs 0)  2  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.33{col 48}{space 2}   0.5636
{txt}{space 10}(2 vs 0)  3  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.86{col 48}{space 2}   0.3542
{txt}{space 10}(2 vs 0)  4  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.38{col 48}{space 2}   0.2408
{txt}{space 10}(2 vs 0)  5  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.57{col 48}{space 2}   0.2104
{txt}{space 10}(2 vs 0)  6  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.62{col 48}{space 2}   0.2030
{txt}{space 10}(2 vs 0)  7  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.64{col 48}{space 2}   0.1999
{txt}{space 10}(2 vs 0)  8  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.66{col 48}{space 2}   0.1970
{txt}{space 10}(2 vs 0)  9  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.68{col 48}{space 2}   0.1943
{txt}{space 10}(2 vs 0) 10  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.69{col 48}{space 2}   0.1935
{txt}{space 10}(2 vs 0) 11  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.66{col 48}{space 2}   0.1972
{txt}{space 10}(2 vs 0) 12  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.58{col 48}{space 2}   0.2092
{col 1}{text}                Joint {col 24}{c |}{result}  (not testable)
{col 1}{text}{hline 23}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}   Contrast{col 36}   std. err.{col 48}     [95% con{col 61}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_interstate_catD@_at {c |}
{space 10}(2 vs 0)  1  {c |}{col 24}{res}{space 2}-.0019388{col 36}{space 2} .0038472{col 47}{space 5}-.0094792{col 61}{space 3} .0056015
{txt}{space 10}(2 vs 0)  2  {c |}{col 24}{res}{space 2}-.0021368{col 36}{space 2} .0037004{col 47}{space 5}-.0093895{col 61}{space 3} .0051159
{txt}{space 10}(2 vs 0)  3  {c |}{col 24}{res}{space 2}-.0030651{col 36}{space 2} .0033087{col 47}{space 5}  -.00955{col 61}{space 3} .0034198
{txt}{space 10}(2 vs 0)  4  {c |}{col 24}{res}{space 2}-.0040427{col 36}{space 2} .0034468{col 47}{space 5}-.0107983{col 61}{space 3} .0027128
{txt}{space 10}(2 vs 0)  5  {c |}{col 24}{res}{space 2}-.0048698{col 36}{space 2} .0038881{col 47}{space 5}-.0124904{col 61}{space 3} .0027507
{txt}{space 10}(2 vs 0)  6  {c |}{col 24}{res}{space 2}-.0055447{col 36}{space 2} .0043554{col 47}{space 5}-.0140812{col 61}{space 3} .0029918
{txt}{space 10}(2 vs 0)  7  {c |}{col 24}{res}{space 2}-.0060655{col 36}{space 2}  .004732{col 47}{space 5}  -.01534{col 61}{space 3} .0032091
{txt}{space 10}(2 vs 0)  8  {c |}{col 24}{res}{space 2}-.0064304{col 36}{space 2} .0049841{col 47}{space 5}-.0161991{col 61}{space 3} .0033382
{txt}{space 10}(2 vs 0)  9  {c |}{col 24}{res}{space 2}-.0066378{col 36}{space 2} .0051143{col 47}{space 5}-.0166616{col 61}{space 3}  .003386
{txt}{space 10}(2 vs 0) 10  {c |}{col 24}{res}{space 2}-.0066857{col 36}{space 2} .0051423{col 47}{space 5}-.0167644{col 61}{space 3} .0033929
{txt}{space 10}(2 vs 0) 11  {c |}{col 24}{res}{space 2}-.0065725{col 36}{space 2} .0050972{col 47}{space 5}-.0165628{col 61}{space 3} .0034178
{txt}{space 10}(2 vs 0) 12  {c |}{col 24}{res}{space 2}-.0062964{col 36}{space 2} .0050138{col 47}{space 5}-.0161232{col 61}{space 3} .0035305
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE D2B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL D2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(#3, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. 
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2B.04-10-2025.gph} saved

{com}. *
. *
. *
. * [MODEL D2: ABSOLUTE TYPE I ERROR RATE] FIGURE D2C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_catD==2) & LOW COMPLEXITY (t1_extbenefits_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_diffoccupseek_catD if t1_diffoccupseek_catD==0|t1_diffoccupseek_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,037}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
t1_diffoccupseek_catD@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     8.42{col 51}{space 2}   0.0037
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     8.51{col 51}{space 2}   0.0035
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     7.00{col 51}{space 2}   0.0082
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.69{col 51}{space 2}   0.0546
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.85{col 51}{space 2}   0.1742
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.06{col 51}{space 2}   0.3042
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.72{col 51}{space 2}   0.3956
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.59{col 51}{space 2}   0.4410
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.58{col 51}{space 2}   0.4466
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.65{col 51}{space 2}   0.4212
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.79{col 51}{space 2}   0.3748
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.99{col 51}{space 2}   0.3188
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_diffoccupseek_catD@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2} .0098655{col 39}{space 2} .0033997{col 50}{space 5} .0032021{col 64}{space 3} .0165288
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2} .0095225{col 39}{space 2} .0032645{col 50}{space 5} .0031242{col 64}{space 3} .0159208
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2}  .007982{col 39}{space 2} .0030169{col 50}{space 5}  .002069{col 64}{space 3}  .013895
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2}  .006497{col 39}{space 2} .0033809{col 50}{space 5}-.0001294{col 64}{space 3} .0131234
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2} .0053775{col 39}{space 2} .0039575{col 50}{space 5} -.002379{col 64}{space 3} .0131341
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2} .0045906{col 39}{space 2} .0044682{col 50}{space 5} -.004167{col 64}{space 3} .0133482
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2} .0041033{col 39}{space 2} .0048307{col 50}{space 5}-.0053647{col 64}{space 3} .0135714
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2} .0038827{col 39}{space 2} .0050392{col 50}{space 5} -.005994{col 64}{space 3} .0137593
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2} .0038957{col 39}{space 2} .0051187{col 50}{space 5}-.0061367{col 64}{space 3} .0139281
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2} .0041095{col 39}{space 2} .0051094{col 50}{space 5}-.0059047{col 64}{space 3} .0141237
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2} .0044911{col 39}{space 2} .0050601{col 50}{space 5}-.0054265{col 64}{space 3} .0144088
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2} .0050075{col 39}{space 2} .0050231{col 50}{space 5}-.0048375{col 64}{space 3} .0148525
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE D2C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL D2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(#3, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D2.FIGURE D2C.04-10-2025.gph} saved

{com}. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H2 & H4: RELATIVE TYPE I ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION ***
. 
. 
. 
. 
. *** ESTIMATE MODEL D3: RELATIVE TYPE I ERROR RATE [MODEL 3 with 2002-2004 & 2020-2022 YEARS OMITTED FROM ESTIMATION SAMPLE: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [PLUS STATE, YEAR, and ADOPTION YEAR COHORT UNIT EFFECTS] ***  (FIGURES D2E-D2F) 
. 
. 
. npregress series relt1error_rat  itmod_monthcount  i.relt1_interstate_catD   i.relt1_diffoccupseek_catD   if itmod_adopt_state==1 & stateid!=51 & stateid!=52 & year<2020, asis(demgovparty repgovparty ln_workload automationrate  ln_uiadmin_budget_real  benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat   i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2}-.2234516{col 40}{space 2} .0292843{col 51}{space 1}   -7.63{col 60}{space 3}0.000{col 68}{space 4}-.2808479{col 81}{space 3}-.1660554
{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.2140715{col 40}{space 2} .0464046{col 51}{space 1}   -4.61{col 60}{space 3}0.000{col 68}{space 4}-.3050228{col 81}{space 3}-.1231202
{txt}{space 23}31  {c |}{col 28}{res}{space 2}-.2450966{col 40}{space 2} .1117441{col 51}{space 1}   -2.19{col 60}{space 3}0.028{col 68}{space 4} -.464111{col 81}{space 3}-.0260822
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0649226{col 40}{space 2} .0355676{col 51}{space 1}   -1.83{col 60}{space 3}0.068{col 68}{space 4}-.1346339{col 81}{space 3} .0047888
{txt}{space 23}35  {c |}{col 28}{res}{space 2}-.2156662{col 40}{space 2} .0576187{col 51}{space 1}   -3.74{col 60}{space 3}0.000{col 68}{space 4}-.3285967{col 81}{space 3}-.1027356
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .0167559{col 40}{space 2} .0546214{col 51}{space 1}    0.31{col 60}{space 3}0.759{col 68}{space 4}   -.0903{col 81}{space 3} .1238118
{txt}{space 23}40  {c |}{col 28}{res}{space 2} -.095243{col 40}{space 2} .0329971{col 51}{space 1}   -2.89{col 60}{space 3}0.004{col 68}{space 4}-.1599161{col 81}{space 3}-.0305698
{txt}{space 23}42  {c |}{col 28}{res}{space 2}-.1634815{col 40}{space 2} .0300496{col 51}{space 1}   -5.44{col 60}{space 3}0.000{col 68}{space 4}-.2223777{col 81}{space 3}-.1045854
{txt}{space 23}44  {c |}{col 28}{res}{space 2}-.2217943{col 40}{space 2} .0478295{col 51}{space 1}   -4.64{col 60}{space 3}0.000{col 68}{space 4}-.3155385{col 81}{space 3}-.1280502
{txt}{space 23}46  {c |}{col 28}{res}{space 2}-.0337714{col 40}{space 2} .0336076{col 51}{space 1}   -1.00{col 60}{space 3}0.315{col 68}{space 4} -.099641{col 81}{space 3} .0320982
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.1948497{col 40}{space 2} .0431106{col 51}{space 1}   -4.52{col 60}{space 3}0.000{col 68}{space 4} -.279345{col 81}{space 3}-.1103545
{txt}{space 23}50  {c |}{col 28}{res}{space 2}-.0758644{col 40}{space 2} .0544206{col 51}{space 1}   -1.39{col 60}{space 3}0.163{col 68}{space 4}-.1825268{col 81}{space 3} .0307979
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2}-.0061452{col 40}{space 2} .0217234{col 51}{space 1}   -0.28{col 60}{space 3}0.777{col 68}{space 4}-.0487222{col 81}{space 3} .0364319
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0481256{col 40}{space 2}  .022786{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4}-.0927853{col 81}{space 3}-.0034659
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0494154{col 40}{space 2} .0232479{col 51}{space 1}   -2.13{col 60}{space 3}0.034{col 68}{space 4}-.0949805{col 81}{space 3}-.0038504
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0570432{col 40}{space 2} .0234986{col 51}{space 1}   -2.43{col 60}{space 3}0.015{col 68}{space 4}-.1030997{col 81}{space 3}-.0109868
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0703458{col 40}{space 2} .0243156{col 51}{space 1}   -2.89{col 60}{space 3}0.004{col 68}{space 4}-.1180035{col 81}{space 3}-.0226881
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0675872{col 40}{space 2}  .024519{col 51}{space 1}   -2.76{col 60}{space 3}0.006{col 68}{space 4}-.1156436{col 81}{space 3}-.0195308
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.0207478{col 40}{space 2}  .028101{col 51}{space 1}   -0.74{col 60}{space 3}0.460{col 68}{space 4}-.0758247{col 81}{space 3} .0343292
{txt}{space 21}2010  {c |}{col 28}{res}{space 2}-.0007053{col 40}{space 2} .0286465{col 51}{space 1}   -0.02{col 60}{space 3}0.980{col 68}{space 4}-.0568514{col 81}{space 3} .0554408
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}-.0367218{col 40}{space 2}  .028158{col 51}{space 1}   -1.30{col 60}{space 3}0.192{col 68}{space 4}-.0919104{col 81}{space 3} .0184668
{txt}{space 21}2012  {c |}{col 28}{res}{space 2}-.0145546{col 40}{space 2} .0281539{col 51}{space 1}   -0.52{col 60}{space 3}0.605{col 68}{space 4}-.0697351{col 81}{space 3} .0406259
{txt}{space 21}2013  {c |}{col 28}{res}{space 2}-.0713637{col 40}{space 2} .0295756{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4}-.1293308{col 81}{space 3}-.0133966
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.0600555{col 40}{space 2}  .028656{col 51}{space 1}   -2.10{col 60}{space 3}0.036{col 68}{space 4}-.1162202{col 81}{space 3}-.0038908
{txt}{space 21}2015  {c |}{col 28}{res}{space 2}-.0389472{col 40}{space 2} .0301153{col 51}{space 1}   -1.29{col 60}{space 3}0.196{col 68}{space 4}-.0979722{col 81}{space 3} .0200778
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}-.0339554{col 40}{space 2} .0295991{col 51}{space 1}   -1.15{col 60}{space 3}0.251{col 68}{space 4}-.0919686{col 81}{space 3} .0240578
{txt}{space 21}2017  {c |}{col 28}{res}{space 2}-.0251687{col 40}{space 2} .0313031{col 51}{space 1}   -0.80{col 60}{space 3}0.421{col 68}{space 4}-.0865217{col 81}{space 3} .0361843
{txt}{space 21}2018  {c |}{col 28}{res}{space 2}-.0241796{col 40}{space 2} .0328897{col 51}{space 1}   -0.74{col 60}{space 3}0.462{col 68}{space 4}-.0886423{col 81}{space 3} .0402831
{txt}{space 21}2019  {c |}{col 28}{res}{space 2}-.0568583{col 40}{space 2}  .033498{col 51}{space 1}   -1.70{col 60}{space 3}0.090{col 68}{space 4}-.1225131{col 81}{space 3} .0087964
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .2179701{col 40}{space 2} .1116143{col 51}{space 1}    1.95{col 60}{space 3}0.051{col 68}{space 4}-.0007899{col 81}{space 3} .4367302
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .2265669{col 40}{space 2} .0489398{col 51}{space 1}    4.63{col 60}{space 3}0.000{col 68}{space 4} .1306467{col 81}{space 3}  .322487
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .1720697{col 40}{space 2} .0528685{col 51}{space 1}    3.25{col 60}{space 3}0.001{col 68}{space 4} .0684494{col 81}{space 3} .2756899
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .0295026{col 40}{space 2}  .034571{col 51}{space 1}    0.85{col 60}{space 3}0.393{col 68}{space 4}-.0382553{col 81}{space 3} .0972604
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2}-.0965551{col 40}{space 2}  .037325{col 51}{space 1}   -2.59{col 60}{space 3}0.010{col 68}{space 4}-.1697108{col 81}{space 3}-.0233994
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2}-.0840596{col 40}{space 2}  .032861{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4}-.1484661{col 81}{space 3}-.0196532
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2} .0265649{col 40}{space 2} .0273755{col 51}{space 1}    0.97{col 60}{space 3}0.332{col 68}{space 4}-.0270901{col 81}{space 3}   .08022
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2} .0569109{col 40}{space 2} .0383063{col 51}{space 1}    1.49{col 60}{space 3}0.137{col 68}{space 4}-.0181681{col 81}{space 3} .1319899
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.1133431{col 40}{space 2} .0362394{col 51}{space 1}   -3.13{col 60}{space 3}0.002{col 68}{space 4} -.184371{col 81}{space 3}-.0423153
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2}-.0139112{col 40}{space 2}  .035429{col 51}{space 1}   -0.39{col 60}{space 3}0.695{col 68}{space 4}-.0833508{col 81}{space 3} .0555285
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0061838{col 40}{space 2} .0420073{col 51}{space 1}   -0.15{col 60}{space 3}0.883{col 68}{space 4}-.0885166{col 81}{space 3} .0761491
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.1204566{col 40}{space 2} .0484981{col 51}{space 1}   -2.48{col 60}{space 3}0.013{col 68}{space 4}-.2155112{col 81}{space 3} -.025402
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m3d if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m3d if e(sample), residuals
{res}{txt}(6,753 missing values generated)

{com}. 
. gen sse_m3d = predsy_m3d * predsy_m3d if e(sample)
{txt}(6,753 missing values generated)

{com}. gen ssr_m3d = residsy_m3d * residsy_m3d if e(sample)
{txt}(6,753 missing values generated)

{com}. 
. egen sum_sse_m3d = total(sse_m3d) if e(sample)
{txt}(6,753 missing values generated)

{com}. egen sum_ssr_m3d = total(ssr_m3d) if e(sample)
{txt}(6,753 missing values generated)

{com}. 
. gen r2_m3d = sum_ssr_m3d/(sum_sse_m3d + sum_ssr_m3d)
{txt}(6,753 missing values generated)

{com}. 
. sum r2_m3d

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m3d {c |}{res}      5,798     .383007           0    .383007    .383007
{txt}
{com}. 
. 
. 
. 
. * [MODEL D3: RELATIVE TYPE I ERROR RATE]  FIGURE D2D:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:5,798}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2865761{col 26}{space 2} .0071335{col 37}{space 1}   40.17{col 46}{space 3}0.000{col 54}{space 4} .2725946{col 67}{space 3} .3005575
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2859786{col 26}{space 2} .0064139{col 37}{space 1}   44.59{col 46}{space 3}0.000{col 54}{space 4} .2734077{col 67}{space 3} .2985496
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2828875{col 26}{space 2} .0045056{col 37}{space 1}   62.79{col 46}{space 3}0.000{col 54}{space 4} .2740567{col 67}{space 3} .2917183
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2789541{col 26}{space 2} .0062073{col 37}{space 1}   44.94{col 46}{space 3}0.000{col 54}{space 4}  .266788{col 67}{space 3} .2911203
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2747838{col 26}{space 2} .0090566{col 37}{space 1}   30.34{col 46}{space 3}0.000{col 54}{space 4} .2570331{col 67}{space 3} .2925345
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2703845{col 26}{space 2} .0116042{col 37}{space 1}   23.30{col 46}{space 3}0.000{col 54}{space 4} .2476407{col 67}{space 3} .2931283
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2657641{col 26}{space 2} .0136464{col 37}{space 1}   19.48{col 46}{space 3}0.000{col 54}{space 4} .2390176{col 67}{space 3} .2925106
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2609306{col 26}{space 2} .0152007{col 37}{space 1}   17.17{col 46}{space 3}0.000{col 54}{space 4} .2311378{col 67}{space 3} .2907233
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2558917{col 26}{space 2} .0163354{col 37}{space 1}   15.66{col 46}{space 3}0.000{col 54}{space 4}  .223875{col 67}{space 3} .2879085
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2506556{col 26}{space 2}  .017134{col 37}{space 1}   14.63{col 46}{space 3}0.000{col 54}{space 4} .2170736{col 67}{space 3} .2842376
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2452301{col 26}{space 2}  .017683{col 37}{space 1}   13.87{col 46}{space 3}0.000{col 54}{space 4}  .210572{col 67}{space 3} .2798881
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .239623{col 26}{space 2} .0180658{col 37}{space 1}   13.26{col 46}{space 3}0.000{col 54}{space 4} .2042148{col 67}{space 3} .2750313
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE D2D{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Error Rate [MODEL D3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2D.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2D.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2D.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. *
. * [MODEL D3: RELATIVE TYPE I ERROR RATE] FIGURE D2E:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_catD==2) & LOW COMPLEXITY (relt1_interstate_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_catD if relt1_interstate_catD==0|relt1_interstate_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,937}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_catD@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.74{col 51}{space 2}   0.0977
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.24{col 51}{space 2}   0.0718
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     4.65{col 51}{space 2}   0.0311
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     4.08{col 51}{space 2}   0.0433
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.30{col 51}{space 2}   0.0691
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.75{col 51}{space 2}   0.0972
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.34{col 51}{space 2}   0.1265
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.97{col 51}{space 2}   0.1608
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.59{col 51}{space 2}   0.2075
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.17{col 51}{space 2}   0.2790
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.73{col 51}{space 2}   0.3935
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.32{col 51}{space 2}   0.5714
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_catD@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2}-.0222767{col 39}{space 2} .0134532{col 50}{space 5}-.0486444{col 64}{space 3} .0040911
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2}-.0233979{col 39}{space 2} .0129947{col 50}{space 5}-.0488671{col 64}{space 3} .0020713
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2}-.0282675{col 39}{space 2} .0131153{col 50}{space 5}-.0539731{col 64}{space 3}-.0025619
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2} -.032535{col 39}{space 2} .0161021{col 50}{space 5}-.0640945{col 64}{space 3}-.0009755
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2}-.0351528{col 39}{space 2} .0193413{col 50}{space 5}-.0730611{col 64}{space 3} .0027554
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2}-.0361945{col 39}{space 2} .0218214{col 50}{space 5}-.0789636{col 64}{space 3} .0065746
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2}-.0357335{col 39}{space 2} .0233824{col 50}{space 5}-.0815622{col 64}{space 3} .0100952
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2}-.0338436{col 39}{space 2}  .024131{col 50}{space 5}-.0811394{col 64}{space 3} .0134522
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2}-.0305982{col 39}{space 2} .0242773{col 50}{space 5}-.0781808{col 64}{space 3} .0169844
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2}-.0260709{col 39}{space 2} .0240832{col 50}{space 5}-.0732731{col 64}{space 3} .0211313
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2}-.0203352{col 39}{space 2} .0238322{col 50}{space 5}-.0670455{col 64}{space 3}  .026375
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2}-.0134649{col 39}{space 2} .0237928{col 50}{space 5}-.0600979{col 64}{space 3} .0331681
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE D2E{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Relative Type I Program Error Rate [MODEL D3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2E.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2E.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2E.04-10-2025.gph} saved

{com}. *
. *
. *
. 
. * [MODEL D3: RELATIVE TYPE I ERROR RATE] FIGURE D2F:   MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catD==2) & LOW COMPLEXITY (relt1_diffoccupseek_catD==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_diffoccupseek_catD if relt1_diffoccupseek_catD==0|relt1_diffoccupseek_catD==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,909}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 29}{c TT}{hline 11}{hline 12}{hline 11}
{col 30}{text}{c |}         df{col 42}        chi2{col 54}     P>chi2
{res}{col 1}{text}{hline 29}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_catD@_at {c |}
{space 16}(2 vs 0)  1  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     3.72{col 54}{space 2}   0.0537
{txt}{space 16}(2 vs 0)  2  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     3.21{col 54}{space 2}   0.0730
{txt}{space 16}(2 vs 0)  3  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.64{col 54}{space 2}   0.4220
{txt}{space 16}(2 vs 0)  4  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.01{col 54}{space 2}   0.9340
{txt}{space 16}(2 vs 0)  5  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.02{col 54}{space 2}   0.8809
{txt}{space 16}(2 vs 0)  6  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.03{col 54}{space 2}   0.8695
{txt}{space 16}(2 vs 0)  7  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.00{col 54}{space 2}   0.9606
{txt}{space 16}(2 vs 0)  8  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.03{col 54}{space 2}   0.8638
{txt}{space 16}(2 vs 0)  9  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.24{col 54}{space 2}   0.6215
{txt}{space 16}(2 vs 0) 10  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.82{col 54}{space 2}   0.3638
{txt}{space 16}(2 vs 0) 11  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     1.91{col 54}{space 2}   0.1668
{txt}{space 16}(2 vs 0) 12  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     3.45{col 54}{space 2}   0.0632
{col 1}{text}                      Joint {col 30}{c |}{result}  (not testable)
{col 1}{text}{hline 29}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 30}{c |}{col 42} Delta-method
{col 30}{c |}   Contrast{col 42}   std. err.{col 54}     [95% con{col 67}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_catD@_at {c |}
{space 16}(2 vs 0)  1  {c |}{col 30}{res}{space 2}  .023443{col 42}{space 2} .0121491{col 53}{space 5}-.0003687{col 67}{space 3} .0472548
{txt}{space 16}(2 vs 0)  2  {c |}{col 30}{res}{space 2} .0208783{col 42}{space 2} .0116473{col 53}{space 5}  -.00195{col 67}{space 3} .0437065
{txt}{space 16}(2 vs 0)  3  {c |}{col 30}{res}{space 2} .0100987{col 42}{space 2} .0125763{col 53}{space 5}-.0145505{col 67}{space 3} .0347479
{txt}{space 16}(2 vs 0)  4  {c |}{col 30}{res}{space 2} .0013949{col 42}{space 2} .0168441{col 53}{space 5} -.031619{col 67}{space 3} .0344087
{txt}{space 16}(2 vs 0)  5  {c |}{col 30}{res}{space 2}-.0031029{col 42}{space 2} .0207074{col 53}{space 5}-.0436888{col 67}{space 3}  .037483
{txt}{space 16}(2 vs 0)  6  {c |}{col 30}{res}{space 2}-.0038291{col 42}{space 2} .0232981{col 53}{space 5}-.0494926{col 67}{space 3} .0418344
{txt}{space 16}(2 vs 0)  7  {c |}{col 30}{res}{space 2} -.001218{col 42}{space 2}  .024652{col 53}{space 5}-.0495352{col 67}{space 3} .0470991
{txt}{space 16}(2 vs 0)  8  {c |}{col 30}{res}{space 2} .0042957{col 42}{space 2}  .025048{col 53}{space 5}-.0447975{col 67}{space 3}  .053389
{txt}{space 16}(2 vs 0)  9  {c |}{col 30}{res}{space 2} .0122779{col 42}{space 2} .0248687{col 53}{space 5}-.0364638{col 67}{space 3} .0610195
{txt}{space 16}(2 vs 0) 10  {c |}{col 30}{res}{space 2} .0222939{col 42}{space 2} .0245512{col 53}{space 5}-.0258255{col 67}{space 3} .0704133
{txt}{space 16}(2 vs 0) 11  {c |}{col 30}{res}{space 2} .0339093{col 42}{space 2} .0245287{col 53}{space 5} -.014166{col 67}{space 3} .0819846
{txt}{space 16}(2 vs 0) 12  {c |}{col 30}{res}{space 2} .0466898{col 42}{space 2} .0251326{col 53}{space 5}-.0025692{col 67}{space 3} .0959489
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE D2F{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Relative Type I Program Error Rate [MODEL D3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2F.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2F.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model D3.FIGURE D2F.04-10-2025.gph} saved

{com}. 
. 
. 
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX D MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Apr 2025, 04:48:06
{txt}{.-}
{smcl}
{txt}{sf}{ul off}